Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	36,316
2	Mississippi	33,451
3	Florida	33,141
4	Alabama	31,958
5	Arizona	30,194
6	North Dakota	29,783
7	Iowa	28,995
8	South Carolina	28,975
9	Georgia	28,670
10	Tennessee	28,653
11	Arkansas	28,424
12	Texas	27,376
13	South Dakota	26,588
14	Nevada	26,389
15	Idaho	24,357
16	Nebraska	24,284
17	New York	23,972
18	Illinois	23,796
19	Rhode Island	23,670
20	New Jersey	23,464
21	Utah	23,448
22	Wisconsin	23,185
23	Oklahoma	22,633
24	District of Columbia	21,853
25	Missouri	21,788
26	Delaware	21,501
27	Kansas	21,411
28	California	20,964
29	Maryland	20,958
30	North Carolina	20,502
31	Massachusetts	19,387
32	Indiana	18,561
33	Minnesota	17,983
34	Virginia	17,536
35	Kentucky	16,690
36	Connecticut	16,351
37	Puerto Rico	15,576
38	New Mexico	14,307
39	Michigan	14,024
40	Ohio	13,414
41	Montana	13,140
42	Pennsylvania	12,987
43	Colorado	12,581
44	Alaska	12,253
45	Washington	12,177
46	Wyoming	10,736
47	West Virginia	9,099
48	Hawaii	9,007
49	Oregon	8,103
50	New Hampshire	6,276
51	Maine	4,067
52	Vermont	2,833

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Dakota	574
2	North Dakota	565
3	Wisconsin	472
4	Montana	380
5	Iowa	343
6	Arkansas	334
7	Nebraska	333
8	Utah	315
9	Idaho	312
10	Kansas	281
11	Oklahoma	281
12	Missouri	266
13	Wyoming	226
14	Mississippi	215
15	Alabama	213
16	Kentucky	197
17	Tennessee	186
18	Illinois	180
19	Indiana	179
20	North Carolina	179
21	Minnesota	172
22	Nevada	172
23	Alaska	164
24	Rhode Island	163
25	Texas	152
26	Louisiana	141
27	Delaware	138
28	Georgia	137
29	New Mexico	134
30	Puerto Rico	133
31	Colorado	115
32	West Virginia	114
33	Florida	112
34	Ohio	111
35	Maryland	105
36	Michigan	105
37	South Carolina	100
38	Massachusetts	98
39	Connecticut	90
40	California	87
41	Virginia	84
42	Pennsylvania	79
43	New Jersey	77
44	Washington	75
45	Hawaii	74
46	New Hampshire	73
47	Arizona	72
48	Oregon	69
49	New York	68
50	District of Columbia	58
51	Maine	32
52	Vermont	10

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,816
2	New York	1,685
3	Massachusetts	1,376
4	Connecticut	1,265
5	Louisiana	1,192
6	Rhode Island	1,055
7	Mississippi	1,007
8	District of Columbia	891
9	Arizona	782
10	Michigan	711
11	Illinois	710
12	Florida	677
13	South Carolina	662
14	Delaware	659
15	Georgia	654
16	Maryland	653
17	Pennsylvania	644
18	Texas	562
19	Indiana	543
20	Nevada	523
21	Alabama	520
22	Arkansas	460
23	Iowa	436
24	New Mexico	423
25	Ohio	419
26	California	406
27	Virginia	380
28	Minnesota	374
29	Tennessee	364
30	Colorado	359
31	Missouri	359
32	North Dakota	350
33	North Carolina	345
34	New Hampshire	325
35	Washington	294
36	Kentucky	275
37	Idaho	267
38	South Dakota	267
39	Oklahoma	263
40	Nebraska	257
41	Kansas	239
42	Wisconsin	234
43	Puerto Rico	210
44	West Virginia	198
45	Montana	174
46	Utah	147
47	Oregon	134
48	Maine	105
49	Hawaii	99
50	Vermont	92
51	Wyoming	91
52	Alaska	72

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	10
2	Florida	6
3	Kansas	5
4	South Dakota	5
5	Arkansas	4
6	Mississippi	4
7	Tennessee	4
8	Georgia	3
9	Iowa	3
10	Louisiana	3
11	Massachusetts	3
12	Missouri	3
13	North Carolina	3
14	Ohio	3
15	South Carolina	3
16	Texas	3
17	Alabama	2
18	Arizona	2
19	California	2
20	Delaware	2
21	Idaho	2
22	Illinois	2
23	Indiana	2
24	Kentucky	2
25	Minnesota	2
26	Montana	2
27	Oklahoma	2
28	Virginia	2
29	Wisconsin	2
30	District of Columbia	1
31	Hawaii	1
32	Michigan	1
33	Nevada	1
34	New Mexico	1
35	Pennsylvania	1
36	Puerto Rico	1
37	Rhode Island	1
38	Utah	1
39	West Virginia	1
40	Wyoming	1
41	Alaska	0
42	Colorado	0
43	Connecticut	0
44	Maine	0
45	Maryland	0
46	Nebraska	0
47	New Hampshire	0
48	New Jersey	0
49	New York	0
50	Oregon	0
51	Vermont	0
52	Washington	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	157,171	1	99
Chattahoochee	Georgia	153,846	2	99
Trousdale	Tennessee	149,061	3	99
Lafayette	Florida	146,402	4	99
Lake	Tennessee	132,269	5	99
Davidson	Tennessee	41,974	203	93
Richland	South Carolina	37,334	288	90
York	South Carolina	19,635	1222	61
Orange	California	17,523	1413	55
Pierce	Washington	10,031	2272	27

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	5,085	1	99
Kenedy	Texas	4,950	2	99
Emporia city	Virginia	4,863	3	99
Galax city	Virginia	4,727	4	99
Randolph	Georgia	4,279	5	99
Richland	South Carolina	596	712	77
Davidson	Tennessee	451	1008	67
Orange	California	403	1118	64
York	South Carolina	270	1499	52
Pierce	Washington	253	1552	50

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.